Systems and methods for neuromodulation device  coding with trans-species libraries

ABSTRACT

Aspects of the invention include systems and methods for neuromodulation device coding with a neural code library. The method includes identifying a pattern of a sensory signal, retrieving one or more digital representations of a neural response corresponding to the identified pattern from a neural code library, where the neural code library comprises a plurality of digital representations of neural responses of neural units corresponding to each pattern, selecting one or more neural units of a patient to be stimulated by a neuromodulation device associated with the patient based on the retrieved one or more digital representations of a neural response pattern, and stimulating the selected neural units of the patient.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims the benefit, pursuant to 35 U.S.C. §119(e), ofU.S. provisional patent application Ser. No. 62/013,853, filed Jun. 18,2014, entitled “COCHLEAR IMPLANT CODING WITH TRANS-SPECIES LIBRARIES,”by Chris Heddon et al., which is incorporated herein by reference in itsentirety.

Some references, which may include patents, patent applications andvarious publications, are cited in a reference list and discussed in thedescription of this invention. The citation and/or discussion of suchreferences is provided merely to clarify the description of theinvention and is not an admission that any such reference is “prior art”to the invention described herein. All references cited and discussed inthis specification are incorporated herein by reference in theirentireties and to the same extent as if each reference was individuallyincorporated by reference.

STATEMENT AS TO RIGHTS UNDER FEDERALLY-SPONSORED RESEARCH

This invention was made with government support under R01 DC011855awarded by the National Institutes of Health. The government has certainrights in the invention.

FIELD OF THE INVENTION

The invention relates generally to the neural stimulation, particularlyto systems and methods for neuromodulation device coding withtrans-species libraries, and more particularly to systems and methodsfor cochlear implant coding with trans-species neural code libraries.

BACKGROUND OF THE INVENTION

The background description provided herein is for the purpose ofgenerally presenting the context of the invention. The subject matterdiscussed in the background of the invention section should not beassumed to be prior art merely as a result of its mention in thebackground of the invention section. Similarly, a problem mentioned inthe background of the invention section or associated with the subjectmatter of the background of the invention section should not be assumedto have been previously recognized in the prior art. The subject matterin the background of the invention section merely represents differentapproaches, which in and of themselves may also be inventions. Work ofthe presently named inventors, to the extent it is described in thebackground of the invention section, as well as aspects of thedescription that may not otherwise qualify as prior art at the time offiling, are neither expressly nor impliedly admitted as prior artagainst the invention.

Cochlear implants are considered the most successful neural prostheses.They restore some hearing for more than 320,000 severe-to-profound deafindividuals by stimulating segments along the length of thetonotopically organized cochlear spiral ganglion. The devices includethree essential components, the speech processor, the transcutaneoustransmitter and receiver, and the cochlear implant array. The devicesrecord and process sound signals and convert acoustic information of thesound signals into electric pulse trains that are used to directlystimulate the auditory nerve. Over the last two decades, the improvementof the coding strategies for acoustic information was the maincontributor to the improvement of the cochlear implants. Still, roomexists to advance the technology.

Communication describes the transfer of information between individuals,such as speech. Acoustic information is processed at a rapid speed bythe brain, at about three to seven syllables per second (Edwards andChang, 2013; Prather, 2013; Scott and McGettigan, 2013; Wang, 2013).Vocal cords are set in vibration by airflow. The vibrations aremodulated by the actions of the larynx, pharynx, lips, teeth, tong, andthe upper airway. Changes in frequency and intensity are used to codeinformation in an acoustic signal. The listener uses the resultingacoustic patterns, or acoustic cues, to decode the information. Complexsignal processing by the brain allows speech perception underchallenging listening conditions. At the other end of the transmissionline, the ear decodes the acoustic signal (Clark, 1995; Clark, 2003).The inner ear acts as a frequency analyzer and converts acousticallyinduced vibrations of the inner ear soft tissue structures into seriesof action on the auditory nerve (von Békésy, 1960; Dallos, 1973; Davis,1983; Hudspeth, 1989 Oct 5; Dallos, 1992; Dallos, 2003). The brain canuse the information provided for communication. As indicated, acousticinformation comprises of complex acoustic patterns that are constantlychanging. According to the articulation different classes can bedistinguished, vowels, semivowels, diphthongs, nasal consonants, stops,fricatives and affricatives. Several theories have been developed of howthe information is processed, including the active theories such as the“Motor Theory” by Liberman, and the “Analysis-by-Synthesis Theory” byStevens and Halle and the passive theories that emphasize speechperception the passive filtering of the acoustic signal by the listener.Moreover the “Quantal Theory”, and the “Action Theory” have beenproposed.

Vocoders are systems that analyze, transmit and synthesize speech. Theyprovided the basis for the development of cochlear implant speechprocessors. One of the early systems was presented by Dudley (Dudley,1939). The system includes a set of bandpass filters, and the amplitudein each of the filters was measured continuously, as was the fundamentalfrequency of the speech. The responses from the bandpass filters wereused to control the output of the system. About 2 to 4 filter bands wererequired for intelligible speech in quiet listening environments(Shannon, Fu, and Galvin, 2004). However, for more challenging listeningconditions or for music perception more independent channels arerequired to code the acoustic information; 16 and more for speech innoise and 30 and more for music perception (Shannon et al., 2004). Thenumber of electrode contacts in contemporary cochlear implants isrelated to the number of critical bands for optimal speech transmission,about 14 to 19 critical bands over the speech frequency range.

In initial attempts to encode the acoustic information, a singlestimulation electrode was used (for a review see Clark, 2003). Whilethis simple coding strategy provided the patients with information aboutsyllables, words, phrases, and sentences, insufficient information wasavailable to discriminate formants and their transitions. Single wordscould be recognized but understanding of running speech was not possiblefor the first implantees. Research following the first implantation ofsingle channel (Djourno and Eyries, 1957) and a multichannel device(Doyle et al., 1963) showed that cochlear implants should bemultichannel devices inserted into scala tympani (Clark, Dowell, et al.,1984; Clark, Tong, et al., 1984; House and Berliner, 1982; House andEdgerton, 1982; Simmons, Dent, and Van Compernolle, 1986; Simmons,Mathews, Walker, and White, 1979). Initial results were improved byemphasizing the mid and high frequency cues (Edgerton and Brimacombe,1984). A similar system was implemented at Vienna (Hochmair et al.,1979). The system included gain compression, followed by frequencyequalization. While single words could be recognized, open-set speechrecognition was not possible in any of the single channel devices.

Multichannel coding strategies were developed to model the tonotopicorganization of the cochlea. For the selection of number of channels,the results from the vocoders were used. However, early attempts tostimulate at all channels simultaneously resulted in unpredictablechanges in loudness (Shannon, 1981). To avoid interaction betweenchannels, electrical stimuli were presented as pulses andnon-simultaneously at neighboring electrode contacts (Shannon, 1981,1983, 1985). While early coding strategies suffered from channelinteractions by stimulating at neighboring electrodes, novel strategieswere tested to overcome existing limitations in stimulation strategiesand resulted to the introduction of the continuous interleaved sampler(CIS) coding strategy (Lawson, Wilson, & Finley, 1993; Wilson, 1997;Wilson et al., 1991; Wilson, Finley, Lawson, Wolford, and Zerbi, 1993).

The continuous interleaved sampler (CIS) coding strategy is inwidespread use amongst current cochlear implants (Wilson and Dorman,2008). CIS processing utilizes band pass filters, then compress theenvelope signals extracted from these filters to map the large dynamicrange up to 100 dB to the smaller range of electrically evoked hearing,which is about 10 dB (Wilson and Dorman, 2008). The outputted trains ofelectrical pulses are then sent to tonotopically placed electrodes tomimic the frequency mapping of a normal cochlea (Wilson and Dorman,2008; Flint, 2010). The amplitude of the transmitted pulse is determinedby the amplitude of the original pulse from the acoustic signal (Flint,2010). Cochlear implants seek to independently stimulate neuron sites toallow for the best speech perception, though studies suggest that nomore than 4-8 independent sites can be stimulated in many electrodedesigns (Fishman, 1997; Wilson, 1997; Kiefer et al., 2000; Garnham etal., 2002). The CIS strategy attempts to avoid the issue of electricalinterference and stimulate more independent areas through transmittingthe pulse trains across electrodes in an interleaved non-simultaneousmanner, such that there is a temporal offset between stimuli (Wilson etal., 1991 and Wilson and Dorman, 2008). Additionally, the brief pulsesare transmitted at a high rate (typically about 1500 pulses/s), whichallows for the preservation of temporal fine structure of the acousticsignal (Somek, 2006; Wilson and Dorman, 2008).

While the pattern of delivering the electrical pules at each contact ofthe cochlear implant electrode is on part of the coding strategy, theselection of the acoustic information to be presented constitutes thesecond part of the coding strategy.

The selection algorithms include Spectral Peak Extraction (SPEAK) codingand Advanced Combination Encoder (ACE) coding strategy and itsvariations. SPEAK sends the incoming signal through a bandpass filter,and then takes approximately 6 ms to scan the output of those frequencyfilters and selects for transmission to the cochlea the 6 filters withthe most energy, that is the frequencies with the most amplitude or thehighest spectral peaks (Somek, 2006). Electrodes are then stimulated ina basal to apical direction. About 6 t o 8 electrodes are usuallystimulated, but the more electrodes stimulated, the slower the rate oftransmission of the outgoing signal. The Advanced Combination Encoder(ACE) coding strategy is very similar to SPEAK except it utilizes higherrates of stimulus, as does CIS, than with the low rate SPEAK strategy(Rubinstein, 2004; Wilson and Dorman, 2008). It was designed to includethe benefits of SPEAK with a high rate CIS. ACE provides for thetransmission of more information to the auditory nerve compared toSPEAK. Pulse rates of 500 to 3500 pulses per second, and a maxima rangefrom 1 to 20 electrodes stimulated simultaneously can be achieved(Flint, 2010).

The auditory sensation that is used to order sound along a scale fromquiet to loud is defined as loudness. It is a subjective sensation,which correlates with sound intensity. Loudness is a subjective measureand changes with frequency. The neurophysiological correlates forloudness are the rate of action potentials at a nerve fiber and therecruitment, the number of neurons, which are exited at the same time.The ears of a normal hearing subject can cover a 120 dB range of soundlevels. Hearing impairment decreases this range drastically and incochlear implant users the range is typically less than 20 dB.

The decrease of the range over which loudness can be coded can beattributed to two facts, the loss of the loss spontaneously activefibers, and the all-or nothing recruitment of auditory nerve fibers inthe current field during electrical stimulation. Auditory nerve fiberswith low spontaneous activity require higher sound levels forstimulation. Combined with the auditory nerve fibers that respond tosoft sounds the entire population of fibers can encode the 120 dB rangein sound levels. Loss of a population of nerve fibers or the synchronousdischarge of neurons at the same time limits the dynamic range ofartificial stimulation.

Pulse repetition rates that are faster than the recovery of an auditoryneuron after an action potential occurred result in more stochasticactivity of the nerve. Similar stochastic neural activity can be seen atthe threshold of stimulation. The latter point is important for thispatent because the novel coding strategy allows the reduction of thecurrent such that stochastic firing pattern occurs. The rate increase isnot achieved by the increase in the current amplitude, but by the pulsegenerator.

Biophysical properties of the cochlea and its solutions determine thecurrent spread during electrical stimulation. For monopolar stimulation,one of the electrodes is placed in the cochlea and the referenceelectrode is located outside the cochlea, interaction occurs not onlyfor close neighboring electrodes. The current spreads for about 3 mmalong the cochlea (the equivalent of about 3 electrode contacts).Multi-polar stimulation paradigms may offer some opportunities to focusthe current field to the target structures or to stimulate at areasbetween two electrode contacts. The price for the selectivity is anincrease in power consumption and the simultaneous use of multipleelectrode contacts. It is not surprising that multipolar stimulation didnot result in drastic improvements in patient performance.

To avoid interactions between neighboring electrodes, contemporarycoding strategies use interleaved stimulation paradigms.Amplitude-modulated trains of electrical pulses at repetition rates atabout, and well above 300 Hz, are used to encode the acousticalinformation. It has been argued that high repetition rates, which arewell above 300 Hz, better reproduce the fine structure of the auditorysignal and that more stochastic activity can be seen that increases therange over which the current level can be changed.

Therefore, a heretofore unaddressed need exists in the art to addressthe aforementioned deficiencies and inadequacies.

SUMMARY OF THE INVENTION

In one aspect, the invention relates to a method for neuromodulationdevice coding for stimulation of a patient.

In one embodiment, the method includes (a) identifying a pattern of asensory signal; (b) retrieving one or more digital representations of aneural response corresponding to the identified pattern from a neuralcode library, wherein the neural code library comprises a plurality ofdigital representations of neural responses of neural unitscorresponding to each pattern; (c) selecting one or more neural units ofa patient to be stimulated by a neuromodulation device associated withthe patient based on the retrieved one or more digital representationsof a neural response pattern; and (d) stimulating the selected neuralunits of the patient.

Further, the foregoing steps (a)-(d) are repeated consecutively for eachpattern of the sensory signal.

In one embodiment, the method may comprise optimizing the selection ofthe units for stimulation optimization based on feedback from thepatient.

In one embodiment, the nueromodulation device is implanted in thepatient. In one embodiment, the neuromodulation device is a cochlearimplant implanted in the patient.

In one embodiment, the sensory signal is an acoustic signal. In oneembodiment, the step of identifying the pattern of the sensory signal isperformed with a speech recognition algorithm.

In one embodiment, the step of retrieving one or more digitalrepresentations of a neural response corresponding to the identifiedpattern from the neural code library is performed with a stochasticmodel including but not limited to a Hidden Markov Model (HMM).

In one embodiment, the plurality of digital representations of neuralresponses of neural units are derived from the measured neural responsesof neural units in a normal functioning animal. The normal functioninganimal includes, but is not limited to, a normal hearing animal.

In one embodiment, each neural unit is an identifiable and stable singleunit in at least one of the inferior colliculus, cochlear nucleus,auditory nerve, haptics, brainstem, and midbrain of the normal hearingfunctioning.

In one embodiment, the neural responses of neural units of the normalfunctioning animal are acquired from neural recordings in the neuralunits when each pattern is played to the normal functioning animal at aplurality of frequencies.

In one embodiment, each neural response pattern contains a sequence ofneural responses of a corresponding neural unit at its correspondingbest frequency to the identified pattern.

In one embodiment, the neural response of each neural unit of the normalhearing animal comprises a train of action potentials generated in theneural unit of the normal functioning animal in response to the receiptof the acoustical pattern.

In one embodiment, the selected neural units of the patient aresimulated with electrical pulses that are coincident with the trains ofaction potentials generated in the corresponding neural units of thenormal functioning animal in response to the receipt of the identifiedpattern.

In one embodiment, the pattern is a segment or a frame of thesensorysignal. In one embodiment, the pattern comprises a phoneme, asyllable, a word, words, or a combination thereof.

In another aspect, the invention relates to a method for cochlearimplant coding for stimulation of a patient. In one embodiment, themethod includes (a) identifying an acoustical pattern of an acousticalsignal; (b) retrieving a set of neural response patterns correspondingto the identified acoustical pattern from a neural code library, whereinthe neural code library comprises neural responses of neural units of atleast one normal hearing animal to each acoustical pattern at aplurality of frequencies, where each neural response pattern contains asequence of neural responses of a corresponding neural unit at itscorresponding best frequency to the identified acoustical pattern; (c)selecting units to be stimulated according to electrode contacts of acochlear implant implanted in the patient and corresponding neuralresponse patterns from the retrieved set of neural response patterns;and (d) applying pulses according to the selected neural responsepatterns to the electrodes of the cochlear implant to stimulate theselected units of the patient for sensing the acoustical pattern.Further, the foregoing steps (a)-(d) are repeated consecutively for eachacoustical pattern of the acoustical signal.

In one embodiment, the method may comprise optimizing the selection ofthe units for stimulation optimization based on feedback from thepatient.

In yet another aspect, the invention relates to a system for cochlearimplant coding for stimulation of a patient. In one embodiment, thesystem has a neural code library, a sensory device, an analyzer, aprocessing device, and a transmitting device.

The neural code library comprises a plurality of digital representationsof neural responses of neural units of at least one normal hearinganimal, wherein each of the plurality of digital representations ofneural responses corresponds to one of a plurality of digitalrepresentations of acoustic patterns.

The sensory device is configured to detect an acoustical signal. In oneembodiment, the sensory device comprises one or more microphones.

The analyzer is in communication with the sensory device via a wired orcable connection, or a wireless connection, and is configured toidentify one of the digital representations of acoustic patterns in theneural code library in response to the receipt of the acoustic signal.

The processing device is in communication with the neural code libraryand the analyzer via a wired or cable connection, or a wirelessconnection and configured, for each identified acoustical pattern, toretrieve the digital representations of neural responses of neural unitscorresponding to the identified acoustic pattern from the neural codelibrary, and select one or more neural units of a patient to bestimulated by a cochlear implant implanted in the patient.

The transmitting device is in communication with the processing deviceand the cochlear implant via a wired or cable connection, or a wirelessconnection, and configured, for each identified acoustical pattern, tostimulate the selected one or more neural units of the patient forsensing the acoustical signal.

In one embodiment, the digital representations of the neural responsesinclude data for each of a plurality of frequencies.

In one embodiment, each neural response pattern contains a sequence ofneural responses of a corresponding neural unit at its correspondingbest frequency to the identified acoustic pattern.

In one embodiment, each neural unit is an identifiable and stable singleunit in at least one of the inferior colliculus, cochlear nucleus,auditory nerve, brainstem, and midbrain of the normal hearing animal.

In one embodiment, the neural responses of neural units of the normalhearing animal are acquired from neural recordings in the neural unitswhen each acoustical pattern is played to the normal hearing animal atthe plurality of frequencies.

In one embodiment, the neural response of each neural unit of the normalhearing animal comprises a train of action potentials generated in theneural unit of the normal hearing animal in response to the receipt ofthe acoustical pattern.

In one embodiment, the selected one or more neural units of the patientare stimulated with electrical pulses that are coincident with thetrains of action potentials generated in the corresponding neural unitsof the normal hearing animal in response to the receipt of theacoustical pattern.

In one embodiment, the acoustical pattern is a segment or a frame of theacoustical signal. In one embodiment, the acoustical pattern comprises aphoneme, a syllable, a word, words, or a combination thereof.

In one embodiment, the electrodes of the cochlear implant comprise anelectrode array with multiple electrode contacts.

In one embodiment, the transmitting device is a wireless transmittingdevice. In one embodiment, the wireless transmitting device is aBluetooth wireless transmitter.

In a further aspect, the invention relates to a method for playing anacoustical signal to a patient having a cochlear implant implanted.

In one embodiment, the method includes selecting an acoustical signalwhich the patient wants to listen to; looking up a neural code library,wherein the neural code library comprises a plurality of digitalrepresentations of neural responses of neural units of at least onenormal hearing animal, wherein each of the plurality of digitalrepresentations of neural responses corresponds to one of a plurality ofdigital representations of acoustic signals and comprises a neuralresponse pattern; if the selected acoustical signal exists in the neuralcode library, selecting one or more neural units of a patient to bestimulated by a cochlear implant implanted in the patient; andstimulating the selected one or more neural units of the patient forlistening to the acoustical signal.

In one embodiment, the method further comprises optimizing the selectionof the units for stimulation optimization based on feedback from thepatient.

In one embodiment, each neural unit is an identifiable and stable singleunit in at least one of the inferior colliculus, cochlear nucleus,auditory nerve, brainstem, and midbrain of the normal hearing animal.

In one embodiment, the neural responses of neural units of the normalhearing animal are acquired from neural recordings in the neural unitswhen each acoustical signal is played to the normal hearing animal at aplurality of frequencies.

In one embodiment, each neural response pattern comprises a train ofaction potentials generated in a corresponding neural unit of the normalhearing animal in response to the receipt of the acoustical signal.

In one embodiment, the selected one or more neural units of the patientare stimulated with electrical pulses that are coincident with thetrains of action potentials generated in the corresponding neural unitsof the normal hearing animal in response to the receipt of theacoustical signal.

In yet a further aspect, the invention relates to a system for playingan acoustical signal to a patient having a cochlear implant implanted.

In one embodiment, the system includes a neural code library comprisinga plurality of digital representations of neural responses of neuralunits of at least one normal hearing animal, wherein each of theplurality of digital representations of neural responses corresponds toone of a plurality of digital representations of acoustic patterns andcomprises a neural response pattern; a processing device incommunication with the neural code library, configured to look up theneural code library, and if the selected acoustical signal exists in theneural code library, select one or more neural units of a patient to bestimulated by a cochlear implant implanted in the patient; and atransmitting device in communication with the processing device and thecochlear implant, configured to stimulate the selected one or moreneural units of the patient for listening to the acoustical signal.

In one embodiment, each neural unit is an identifiable and stable singleunit in at least one of the inferior colliculus, cochlear nucleus,auditory nerve, brainstem, and midbrain of the normal hearing animal.

In one embodiment, the neural responses of neural units of the normalhearing animal are acquired from neural recordings in the neural unitswhen each acoustical pattern is played to the normal hearing animal at aplurality of frequencies.

In one embodiment, each neural response pattern comprises a train ofaction potentials generated in a corresponding neural unit of the normalhearing animal in response to the receipt of the acoustical signal.

In one embodiment, he selected one or more neural units of the patientare stimulated with electrical pulses that are coincident with the trainof action potentials generated in the neural unit of the normal hearinganimal in response to the receipt of the acoustical signal.

In one embodiment, the electrodes of the cochlear implant comprise anelectrode array with multiple electrode contacts.

In one embodiment, the processing device is in communication with theneural code library via a wired or cable connection, or a wirelessconnection.

In one embodiment, the transmitting device is in communication with theprocessing device and the cochlear implant via a wired or cableconnection, or a wireless connection.

In one embodiment, the transmitting device is a wireless transmittingdevice. In one embodiment, the wireless transmitting device is aBluetooth wireless transmitter.

In one aspect of the invention, a method for neuromodulation devicecoding for stimulation of an epilepsy patient may be used in connectionwith a closed loop deep brain stimulator. In one embodiment, the methodmay be implemented according to the following steps: identifying apattern of pathological neural activity that heralds the beginning ofepileptic activity; retrieving one or more digital representations of aneural response corresponding to a therapeutic neural response for theidentified pathological neural activity from a neural code library,wherein the neural code library comprises a digital representation ofappropriate therapeutic neural responses that correspond to a libraryrecorded from an animal, or a library algorithmically reconstructed tomimic the library based on the library recorded from an animal;selecting one or more neural units of a patient to be stimulated by thedeep brain stimulator associated with the patient based on the retrievedone or more digital representations of a neural response pattern; andusing the deep brain stimulator to stimulate the selected neural unitsof the patient.

In another aspect of the invention, a method for neuromodulation devicecoding for stimulation of a patient may be used in connection with adeep brain stimulator. In one embodiment, the method may be implementedaccording to the following steps: either the patient or theneuromodulation device selects a desired neural signal from a neuralcode library; retrieving one or more digital representations of a neuralresponse corresponding to the identified pattern from a neural codelibrary, wherein the neural code library comprises a digitalrepresentations of a neural response of neural units corresponding toeach pattern; selecting one or more neural units of a patient to bestimulated by the deep brain stimulator associated with the patientbased on the retrieved one or more digital representations of a neuralresponse pattern; and using the deep brain stimulator to stimulate theselected neural units of the patient.

In yet another aspect of the invention, a method for neuromodulationdevice coding for stimulation of a patient may be used in connectionwith a spinal cord stimulator. In one embodiment, the method may beimplemented according to the following steps: either the patient or theneuromodulation device selects a desired neural signal from a neuralcode library; retrieving one or more digital representations of a neuralresponse corresponding to the identified pattern from a neural codelibrary, wherein the neural code library comprises a digitalrepresentations of a neural response of neural units corresponding toeach pattern; selecting one or more neural units of a patient to bestimulated by the deep brain stimulator associated with the patientbased on the retrieved one or more digital representations of a neuralresponse pattern; and using the spinal cord stimulator to stimulate theselected neural units of the patient.

In a further aspect of the invention, a method for neuromodulationdevice coding for stimulation of a patient may be used in connectionwith a retinal implant. In one embodiment, the method may be implementedaccording to the following steps: a visual signal is analyzed by acomputer vision algorithm which is used to identify and select itemsfrom a scene based upon the patient's preselected scene complexityparameters; the algorithm retrieves one or more digital representationsof the scene items corresponding to the identified pattern from a neuralcode library, wherein the neural code library comprises digitalrepresentations of neural response of neural units corresponding to eachpattern; selecting one or more neural units of a patient to bestimulated by the retinal implant associated with the patient based onthe retrieved one or more digital representations of a neural responsepattern; and using the retinal implant to stimulate the selected neuralunits of the patient.

These and other aspects of the invention will become apparent from thefollowing description of the preferred embodiment taken in conjunctionwith the following drawings, although variations and modificationstherein may be affected without departing from the spirit and scope ofthe novel concepts of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Patent and Trademark Officeupon request and payment of the necessary fee.

The accompanying drawings illustrate one or more embodiments of theinvention and, together with the written description, serve to explainthe principles of the invention. Wherever possible, the same referencenumbers are used throughout the drawings to refer to the same or likeelements of an embodiment.

FIG. 1 shows schematically a flowchart for cochlear implant coding forstimulation of a patient according to one embodiment of the invention.

FIG. 2 shows schematically a system for cochlear implant coding forstimulation of a patient according to one embodiment of the invention.

FIG. 3 shows schematically frequency mapping in the cochlear implantcoding for stimulation of a patient according to one embodiment of theinvention.

FIGS. 4-7 show preliminary experimental data of the cochlear implantcoding for the stimulation for few selected words “bean”, “bomb”,“shirt” and tough”, respectively, according to embodiments of theinvention. In panel (A), the y-axis of the spectrogram represents thelog-frequencies and the x-axis of the spectrogram represents the time;in panel (B), the y-axis of the spectrogram represents the time and thex-axis of the spectrogram represents the log-frequency of the acousticalsignal. The color is a measure for the magnitude, where the darkercolors are for larger magnitudes, and the brighter colors such as yellowand white show low magnitudes. The black dots show correspondingsequences of action potentials that were recorded in the guinea piginferior colliculus, while the word was played to the animal's ear. Thepanel (C) shows the accumulated number of action potentials of the panel(A) in a 10 ms time interval. The panel (D) shows the average rate ofaction potentials for each of the channels (green) and a maximum rate,which was calculated from the shortest time interval between successiveaction potentials in each channel.

FIG. 8 is a table listing the correct words presented during the testingaccording to one embodiment of the invention. It also shows the possibleselections for the test subject and which decisions were made. The dataof 4 test subjects are shown.

FIG. 9 is a table listing the number of correct and wrong selections forall subjects tested even if only partial data sets were available,according to one embodiment of the invention.

FIG. 10 shows schematically a flowchart for playing an acoustical signalto a patient having a cochlear implant implanted according to oneembodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention will now be described more fully hereinafter withreference to the accompanying drawings, in which exemplary embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likereference numerals refer to like elements throughout.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the invention, and in thespecific context where each term is used. Certain terms that are used todescribe the invention are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the invention. For convenience, certainterms may be highlighted, for example using italics and/or quotationmarks. The use of highlighting has no influence on the scope and meaningof a term; the scope and meaning of a term is the same, in the samecontext, whether or not it is highlighted. It will be appreciated thatsame thing can be said in more than one way. Consequently, alternativelanguage and synonyms may be used for any one or more of the termsdiscussed herein, nor is any special significance to be placed uponwhether or not a term is elaborated or discussed herein. Synonyms forcertain terms are provided. A recital of one or more synonyms does notexclude the use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and in no way limits the scope and meaning of theinvention or of any exemplified term. Likewise, the invention is notlimited to various embodiments given in this specification.

It will be understood that, as used in the description herein andthroughout the claims that follow, the meaning of “a”, “an”, and “the”includes plural reference unless the context clearly dictates otherwise.Also, it will be understood that when an element is referred to as being“on” another element, it can be directly on the other element orintervening elements may be present therebetween. In contrast, when anelement is referred to as being “directly on” another element, there areno intervening elements present. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

It will be understood that, although the terms first, second, third etc.may be used herein to describe various elements, components, regions,layers and/or sections, these elements, components, regions, layersand/or sections should not be limited by these terms. These terms areonly used to distinguish one element, component, region, layer orsection from another element, component, region, layer or section. Thus,a first element, component, region, layer or section discussed belowcould be termed a second element, component, region, layer or sectionwithout departing from the teachings of the invention.

Furthermore, relative terms, such as “lower” or “bottom” and “upper” or“top,” may be used herein to describe one element's relationship toanother element as illustrated in the Figures. It will be understoodthat relative terms are intended to encompass different orientations ofthe device in addition to the orientation depicted in the Figures. Forexample, if the device in one of the figures is turned over, elementsdescribed as being on the “lower” side of other elements would then beoriented on “upper” sides of the other elements. The exemplary term“lower”, can therefore, encompasses both an orientation of “lower” and“upper,” depending of the particular orientation of the figure.Similarly, if the device in one of the figures is turned over, elementsdescribed as “below” or “beneath” other elements would then be oriented“above” the other elements. The exemplary terms “below” or “beneath”can, therefore, encompass both an orientation of above and below.

It will be further understood that the terms “comprises” and/or“comprising,” or “includes” and/or “including” or “has” and/or “having”,or “carry” and/or “carrying,” or “contain” and/or “containing,” or“involve” and/or “involving, and the like are to be open-ended, i.e., tomean including but not limited to. When used in this disclosure, theyspecify the presence of stated features, regions, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, regions, integers,steps, operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

As used herein, “around”, “about” or “approximately” shall generallymean within 20 percent, preferably within 10 percent, and morepreferably within 5 percent of a given value or range. Numericalquantities given herein are approximate, meaning that the term “around”,“about” or “approximately” can be inferred if not expressly stated.

As used herein, the phrase “at least one of A, B, and C” should beconstrued to mean a logical (A or B or C), using a non-exclusive logicalOR. It should be understood that one or more operations within a methodis executed in different order (or concurrently) without altering theprinciples of the invention.

As used herein, the term “action potentials” refer to the electricresponse of nerve fibers or muscle tissues to its stimulation such aselectrical stimuli, optical stimuli, and/or acoustic click stimuli. Theaction potentials are considered as the traveling signals of nerves andthe localized changes that contract muscle cells responsive to thestimulation. Compound action potentials are the summation of individualaction potentials from single neurons.

As used herein, the term “nerve fiber” refers to a portion of theneuron, namely the axon, which carries action potentials from one end ofthe neuron to the other. The cochlear nerve fibers originate fromneurons of the spiral ganglion and project peripherally to cochlear haircells and centrally to the cochlear nuclei (cochlear nucleus) of thebrain stem. They mediate the sense of hearing.

The term “cochlea,” as used herein, refers to a spiral-shaped cavity ofthe inner ear that resembles a snail shell and contains nerve endingsessential for hearing. The cochlea includes three fluid-filled chambers:scala tympani and scala vestibuli (both of which contain perilymph), andscala media (which contains endolymph). The scala tympani and the scalavestibuli are contiguous with each other, merging at the tip of thesnail shell, the helicotrema. The stapes transmits vibrations to thefenestra ovalis (oval window) on the outside of the cochlea, whichvibrates the perilymph in the scala vestibuli. This in turn vibrates theendolymph in the scala media, thus causing movements of the hair bundlesof the hair cells, which are acoustic sensor cells that convertvibration into electrical potentials.

The term “cochlear implant”, as used herein, refers to a device that isplaced into the cochlea to provide sound perception for deafindividuals.

Embodiments of the invention are illustrated in detail hereinafter withreference to accompanying drawings. It should be understood thatspecific embodiments described herein are merely intended to explain theinvention, but not intended to limit the invention. In accordance withthe purposes of this invention, as embodied and broadly describedherein, this invention, in certain aspects, relates to a novel codingstrategy, i.e., methods and systems for neuromodulation device codingwith trans-species libraries, and more particularly to systems andmethods for cochlear implant coding with trans-species neural codelibraries.

Improvement of cochlear implant performance over the last decades isbased on improved coding of the acoustic information. Although theincrease of performance has recently plateaued, performance can befurther enhanced with the novel coding strategy. In certain embodiments,the novel coding strategy uses speech recognition algorithms to identifyan acoustic pattern of speech (i.e., an acoustic signal) and matches theacoustic pattern of speech with neural responses, which were recordedfrom the auditory nerve, the cochlear nucleus, or the inferiorcolliculus of normal hearing animals (or normal functioning animals).The neural response patterns are recorded from different sites (neuralunits or neurons) along the cochlea to match the tonotopic organizationof the auditory system. The neural stimulation patterns are used tostimulate the auditory nerve with the cochlear implant for a cochlearimplant user (patient). Once the speech recognition algorithm hasidentified an acoustic pattern such as phoneme, syllable, or word, itmatches this acoustic pattern with a basic set of stimulation patterns.Since for each acoustic pattern, several neural responses exist in theneural code library, ordered by the tonotopic site along the cochlea inwhich they were recorded, a “self learning” model such as but notlimited to Hidden Markov Model (HHM) is used to optimize thedistribution of the patterns along the different contacts of theelectrode array. Among other things, the novel coding strategy providesmore natural patterns of stimulation, decreases the average stimulationrate by about a factor of about 10, has fixed stimulation currents closeto stimulation threshold, provides more stochastic firing pattern of theneurons and increases the number of independent channels for speechcoding.

Among other unique features, the invented coding strategy utilizes aneural code library, i.e., a trans-species library. The neural codelibrary comprises neural responses of neural units of at least onenormal functioning/hearing animal to each acoustic pattern at aplurality of frequencies. In certain embodiments, the neural responsesof neural units of the normal functioning/hearing animal correspondingto each acoustic pattern are represented by a plurality of digitalrepresentations in the neural code library. Each of the plurality ofdigital representations represents a neural response pattern of acorresponding neural unit. The plurality of digital representations ofneural responses of neural units are derived from the measured neuralresponses of neural units in the normal functioning/hearing animal. Tocreate the library, in certain embodiments, neural responses arerecorded from neurons (neural units) in the inferior colliculus, thecochlear nucleus, or the auditory nerve of the normal hearing animalwhile phonemes, syllables, and words are played to the ears of thenormal hearing animal. Generally, a neural unit is an identifiable andstable single unit in at least one of the inferior colliculus, cochlearnucleus, auditory nerve, brainstem, and midbrain of the normal hearinganimal. Neural recordings are done with single tungsten, glasselectrodes, or the like. After surgically gaining access to the targetstructure, the recording electrode is inserted into the tissue and isadvanced until contact with a single neuron has been established. Thebest frequency for each of the neurons is determined before theresponses to the selected phonemes, syllables, and words are recorded.The best frequency of a neural unit is a frequency for pure tonestimulation that needs a lowest sound level to evoke a neural responsein the corresponding unit. By knowing the best frequency of thelocation, the responses can be frequency matched with each of thecontacts of the cochlear implant electrode. The neural response of eachneural unit of the normal hearing animal comprises a train of actionpotentials generated in the neural unit of the normal hearing animal inresponse to the receipt of the acoustic pattern.

The neural code library or trans-species library may be stored in localcomputers, servers, or cloud dives on the internet. Further, more neuralresponse patterns can be added into the neural code library from tine totime. The more the neural response patterns in the neural code library,the more accurate the neural stimulations.

Referring to FIG. 1, a method (flowchart) 100 for cochlear implantcoding for stimulation of a patient is schematically shown according toone embodiment of the invention. In the exemplary embodiment, the methodincludes the following steps: at first, an acoustic pattern of anacoustic signal is identified at step 110.

In certain embodiments, the acoustic pattern is a segment or a frame ofthe acoustic signal. In certain embodiments, the acoustic patterncomprises a phoneme, a syllable, a word, words, or a combinationthereof. In some embodiments, the system continuously records theacoustic information (signal) and converts the acoustic signal into atrack of voltage values obtained from, for example, a microphone.Overlapping frames are acquired. Hereby, each frame is continuouslyshifted every 10 ms or optimized otherwise.

In certain embodiments, speech recognition algorithms are utilized toidentify an acoustic pattern of the acoustic signal and matches theacoustic pattern of the acoustic signal with neural responses. Forexample, a Fast Fourier Transform of the acoustic signal provides theacoustic signal in the frequency domain and allows comparing the patternof the speech signal with patterns stored in the neural code library.

Speech recognition algorithms are based on three approaches, theacoustic phonetic, the pattern recognition, and the artificialintelligence approach. Acoustic phonetic approaches try to find speechsounds and label these sounds. Pattern matching involves two essentialsteps, pattern training and pattern comparison. A speech patternrepresentation can be in the form of a speech template or a statisticalmodel to be applied to either segments shorter than a word, words, orsentences. Today, pattern recognition is the most common method used inspeech recognition. Artificial intelligence to recognize speech relieson elements found in the phonetic and the pattern recognitionapproaches. All approaches rely on the ability to extract features fromthe acoustic signal that can be matched with features found in speechand use a parametric representation of speech rather the time waveformitself To extract features from the signal, method such as the principalcomponent analysis (PCA), linear discriminant analysis (LDA),independent component analysis (ICA), linear predictive coding (LPC)(Davis, 1986; Durand and Pibarot, 1995; Erickson and D′Alfonso, 2002;Kob and Neuschaefer-Rube, 2002; Gaubitch et al., 2006; Stephens andHolt, 2011; Wang et al., 2011), cepstral analysis, Mel-frequency scaleanalysis, filter bank analysis, Mel-frequency cepstrum (MFFCs), kernelbased feature extraction method, wavelet analysis, dynamic featureextraction, (LPC and MFCC) spectral subtraction cepstral meansubtraction, RASTA filtering, or an integrated phoneme subspace method.

Once the acoustic pattern, such as a word, is recognized, it will beused to retrieve corresponding recorded neural response patterns fromthe midbrain, brain stem, or the auditory nerve of an animal in theneural code library. In certain embodiments, the recorded neuralresponse patterns are represented by the digital representations in theneural code library.

At step 120, a set of neural response patterns (i.e., one or moredigital representations) corresponding to the identified acousticpattern is retrieved from the neural code library. Each neural responsepattern contains a sequence of neural responses of a correspondingneural unit at its corresponding best frequency to the acoustic pattern.Since for each acoustic pattern, several neural responses exist in theneural code library, ordered by the tonotopic site along the cochlea inwhich they were recorded, a stochastic model such as a Hidden MarkovModel (HHM) is used to optimize the distribution of the patterns alongthe different contacts of the electrode array. That is, the stimulationpattern in the neural code library is optimally matched with thecontacts of the cochlear implant electrode for any given patient. Numberof contacts used is optimized for speech recognition.

At step 130, one or more neural units (neurons) of a patient to bestimulated are selected based on electrode contacts of a cochlearimplant implanted in the patient and their neural response patterns fromthe retrieved set of neural response patterns. For example, for acochlear implant with 16 electrodes, 16 neural units of the patient areselected. The frequency is matched to the best frequency of each contactof the cochlear implant, which determined for each patient.

FIG. 3 shows schematically frequency mapping from a normal hearing rangeof 20 Hz to 20 KHz of a human to a range of 250 Hz to 8 kHz for acochlear implant and the selection of the neural units of the patientfor stimulation in the cochlear implant coding method according to oneembodiment of the invention. In this exemplary embodiment, the neuralcode library contains recordings of neural response patterns of a normalhearing animal to words, where the best frequencies of more then 100units are identified in the cochlear implant coding, which are in therange of 20 Hz to 20 kHz. However, the electrode contacts of a cochlearimplant, where the neural units at the electrode contacts arestimulated, are usually about 12-22. The frequency range of the cochlearimplant is about 250 Hz to 8 kHz. Therefore, according to embodiments ofthe cochlear implant coding, about 12-22 units are selected from theneural code library in accordance with the electrode contacts of thecochlear implant for stimulation.

Once the one more neural units of the patient are selected, their pulsepatterns are played to the cochlear implant patient via their CI. Atstep 140, the selected neural units of the patient are stimulated forsensing the acoustic pattern based on the selected neural responsepatterns. In one embodiment, the selected neural units of the patientare stimulated with electrical pulses that are coincident with the trainof action potentials generated in the neural unit of the normal hearinganimal in response to the receipt of the acoustic pattern.

In addition, the method may comprises optimizing the selection of theunits for stimulation optimization based on feedback from the patient.

Further, the foregoing steps 110-140 are repeated consecutively for eachacoustic pattern of the acoustic signal.

Referring to FIG. 2, a system 200 for cochlear implant coding forstimulation of a patient is schematically shown according to oneembodiment of the invention. In the embodiment, the system 200 has aneural code library 210, a sensory device 220, an analyzer 230, aprocessing device 240, and a transmitting device 250.

The neural code library 210 comprises a plurality of digitalrepresentations of neural responses of neural units of at least onenormal hearing animal. In one embodiment, the normal hearing animal 211,e.g., a cat, is used to create the library for stimulation. Phonemes,syllables, and words are played via a speaker 213 to the animal 211 andthe corresponding neural responses are recorded either from the auditorynerve, the cochlear nucleus or the inferior colliculus usingmultichannel electrodes. Likewise, repeated recordings can be made withsingle channel electrodes. Hereby, the location of the electrodedetermines the best frequency. For each of the phonemes, syllables orwords, responses are recorded and stored in a library that is associatedwith the acoustic signal.

The sensory device 220 is configured to detect an acoustic signal, forexample, played from a speaker 253. In one embodiment, the sensorydevice 220 comprises one or more microphones.

The analyzer 230 is in communication with the sensory device 220 and theneural library 210 via a wired or cable connection, or a wirelessconnection, and is configured to identify each acoustic pattern of theacoustic signal.

The processing device 240 is in communication with the neural codelibrary 210 and the analyzer 230 via a wired or cable connection, or awireless connection. The processing device 240 is configured, for eachidentified acoustic pattern, to retrieve a set of neural responsepatterns (digital representations of neural responses) corresponding tothe identified acoustic pattern from the neural code library. Eachneural response pattern contains a sequence of neural responses of acorresponding neural unit at its corresponding best frequency to theidentified acoustic pattern. The processing device 240 is alsoconfigured, for each identified acoustic pattern, to select one or moreneural units of the patient to be stimulated corresponding to electrodecontacts (locations of the neural units) of a cochlear implant implantedin the patient and their neural response patterns from the retrieved setof neural response patterns.

The acoustic signal is recorded and the acoustic pattern is determined.Pattern is matched with acoustic pattern in the library. Since eachidentified acoustic pattern is linked to a series of neural patternsthat were recorded from the animal, the recorded neural response can beused to stimulate the auditory nerve of the severe-to-profound deafsubject via the cochlear implant. Hereby, the optimal use of thesequences stored for the acoustic pattern is dynamic and is determinedfor each user with the help of a self-learning stochastic model, such asa Hidden Markov Model.

The transmitting device 250 is in communication with the processingdevice 240 and the cochlear implant 251 via a wired or cable connection,or a wireless connection. The transmitting device 250 is configured, foreach identified acoustic pattern, to transmit the digitalrepresentations of neural responses corresponding to the selected neuralresponse patterns to the electrodes of the cochlear implant as so tostimulate the selected neural units of the patient for sensing theacoustic signal.

According to the invention, to convert the acoustic signal intostimulation patterns of the cochlear implant, the system 200continuously records the acoustic information and converts the acousticsignal into a track of voltage values obtained from the microphone.Overlapping frames are acquired. Hereby, each frame is continuouslyshifted every 10 ms or optimized otherwise. A Fast Fourier Transform ofthe signal provides the signal in the frequency domain and allowscomparing the pattern of the speech signal with patterns stored in alibrary. Once a match is made with a phoneme, syllable, or word, thesystem outputs the corresponding stimulation pattern stored in thelibrary for this word at the cochlear implant electrode. The stimulationpattern includes as many electrodes contacts along the cochlear implantarray as possible. The stimulation library, which is obtained fromneural recordings in a normal hearing animal, contains the neuralresponses from more than 100 possible frequencies for each word.Starting with a given initial distribution for stimulation channels, astochastic model, such as a Hidden Markov Model, will select the optimalstimulation pattern for each word for the patient. The system isindividualized for each patient and provides the best stimulationpattern for each individual and each word/syllable.

FIG. 10 shows schematically a flowchart for playing an acoustic signalto a patient having a cochlear implant implanted according to oneembodiment of the invention. In this exemplary embodiment, the neuralcode library or trans-species library may contain the neural responsepatterns of acoustic signals, i.e., the digital representations ofneural responses of neural units corresponding to the acoustic signal,such as songs, speech, movies, stories, and so on, in additional to theneural response patterns of acoustic patterns such as phoneme, syllable,or word. The neural code library or trans-species library may be storedin local computers, servers, or cloud dives on the internet.

For predetermined music tunes—the tune box. At first, the cochlearimplant user (patient) selects an acoustic signal such a song to beplayed. Then, the neural code library is looked up. The neural codelibrary comprises neural responses of neural units of at least onenormal hearing animal to a plurality of acoustic signals. In oneembodiment, the neural responses of each neural unit of the normalhearing animal are acquired from neural recordings in the correspondingneural unit when each acoustic signal at the plurality of frequencies isplayed to the normal hearing animal. The neural responses of each neuralunit of the normal hearing animal to each acoustic signal compriseneural response patterns. Each neural response pattern comprises a trainof action potentials generated in a corresponding neural unit of thenormal hearing animal responsive to the acoustic signal.

If the selected acoustic signal exists in the neural code library,neural units to be stimulated are selected according to electrodecontacts of the cochlear implant implanted in the patient and thecorresponding neural response patterns from the neural code library.Since for each acoustic signal, several neural responses exist in theneural code library, ordered by the tonotopic site along the cochlea inwhich they were recorded, an HHM is used to optimize the distribution ofthe patterns along the different contacts of the electrode array.

Next, the selected neural units of the patient are stimulated forlistening to the acoustic signal, by applying electrical pulses that arecoincident with the trains of action potentials generated in thecorresponding neural units of the normal hearing animal in response tothe receipt of the acoustic signal.

In certain embodiments, the method further comprises optimizing theselection of the units for stimulation optimization based on feedbackfrom the patient.

In one aspect, the invention also relates to a system for playing anacoustic signal to a patient having a cochlear implant implanted. Thesystem includes the neural code library comprising a plurality ofdigital representations of neural responses of neural units of at leastone normal hearing animal to a plurality of acoustic signals, aprocessing device configured to look up the neural code library, and ifthe selected acoustic signal exists in the neural code library, selectneural units of the patient to be stimulated corresponding to electrodecontacts of the cochlear implant implanted in the patient and thecorresponding neural response patterns (digital representations) fromthe neural code library, and a transmitting device configured totransmit electrical that are coincident with the trains of actionpotentials generated in the corresponding neural units of the normalhearing animal in response to the receipt of the acoustical signal tothe electrodes of the cochlear implant to stimulate the selected unitsof the patient for listening to the acoustic signal.

In certain embodiments, the processing device is in communication withthe neural code library via a wired or cable connection, or a wirelessconnection. The transmitting device is in communication with theprocessing device and the cochlear implant via a wired or cableconnection, or a wireless connection. The transmitting device can be awireless transmitting device, such as a Bluetooth wireless transmitter.

Accordingly, the novel coding strategy, among other things, decreasesthe power consumption of the cochlear implant, decreases the currentspread during stimulation, and provides more stochastic stimulationpattern since stimulation is only done at threshold level. Thestimulation along the cochlea has a more natural pattern since stimulusfrequency and site are determined and optimized by the patient.

Reduction in power consumption: In the normal hearing listener, loudnessis encoded by the increase in rate of action potentials on the auditorynerve and by the recruitment of auditory neurons. Recruitment describesthe number of neurons that produce a response to the acousticstimulation, which is increasing with increasing sound level. Incontemporary devices, loudness is mostly encoded by an increase incurrent amplitude, which results in an increase of the average rate ofaction potentials on the auditory nerve. The dynamic range over whichneurons increase their rate of action potentials is typically 6-12 dB.This is different to the dynamic-range for normal hearing listeners ofabout 120 dB. With the novel coding strategy, the stimulation currentremains constant at threshold level and the increase in rate is achievedby an increase in number of pulses delivered by the cochlear implantsystem. Typically, the average number of action potentials delivered ona selected electrode contact is below 100 Hz. This is well below thetypical average repetition rate of contemporary devices, which is morethan 250 pulses per second.

With the novel coding strategy, the current level is at threshold allthe time. This reduces the power consumption as a result of increasingthe current level with increasing sound levels. The recruitment isachieved by adding more channels for stimulation.

Increase in stochastic firing and increase in selective stimulation:Another advantage of stimulating the neurons at threshold levels is themore stochastic firing of the neurons. Not every neuron is equal in sizeand excitability. Differences in the electrical properties of the cellsare apparent at repetition rates for stimulation that times betweensuccessive pulses are shorter than the refractory period of the neurons,which is about 0.6 ms.

Moreover, at threshold current levels, only a spatially small populationof neurons is activated. Stimulation is more selective and moreindependent channels for stimulation are feasible. Since the currentlevel for stimulation is not changing over time, the average numberneurons stimulated by the current from one electrode contact remainsstable.

These and other aspects of the invention are further described in thefollowing section. Without intend to limit the scope of the invention,further exemplary implementations of the same according to theembodiments of the invention are given below.

Preliminary experiments were conducted according to embodiments of theinvention. 53 words from the commercially available Speech-In-Noise(SIN) test were played to the ear of an anesthetized guinea pig, whileneural activity of an identified and stable single unit in the centralnucleus of the inferior colliculus was recorded. The best frequency ofthe unit to pure tone acoustic stimulation was determined as well. Afterthe conclusion of the experiments, spectrograms of the acoustic signalwere produced, as shown in FIGS. 4-7. It can be seen that for the words“bean”, “bomb”, “shirt” and “tough”, which are shown in the examples,the acoustic information is represented over the frequency range between0.1 and 7 kHz. The black dots in FIGS. 4-7 show the corresponding neuralresponses, which were recorded from the guinea pig inferior colliculus.Each dot represents an action potential and each line of dots (train ofaction potentials) is a different unit. The location of the line alongthe y-axis is determined by the unit's best frequency to the pure tonestimulation. The trains of action potentials are converted into trainsof electrical pulses, where the timing of the electrical pulses are thesame as the occurrence of the action potentials. Because of the memorylimitations of the computer to the cochlear implant interface, theBionic Ear Data Collection System (BEDCS), only up to 10 of those trainsof electrical pulses were selected and were played to cochlear implantusers who participated in the study. All electrode contacts were used tosimultaneously stimulate starting from the tip of the array. The basal 6electrodes were left “empty” (no stimulation occurred via thoseelectrodes). In this test, the natural frequency map of the CI usercould not be reproduced because of the lack of the animal data. What ispresented is a distorted selection of frequencies, missing frequencyinformation above and below 1.9 kHz. Equal speech intelligibility wasfound for speech, which was either high-pass or low-pass filtered atabout 1.9 kHz (French NR, Steinberg JC. 1947; Fletcher HM, Steinberg JC.1929). During the test sessions, the subjects were asked to describe thelexical content and the hearing experience to the words. When the wordswere played with the initial selection of frequencies to the listener,they could identify different tracks. Although lexical information waswrong, the information was robust. For example, subject 2 (S2) couldclearly identify replayed words in the sequences, which were presentedin a random order. S2 volunteered the information that the speaker is amale (which is correct), even though S2 was not asked for thisinformation.

Furthermore, the test subjects could identify rhythm and loudnesschanges of the sequence despite the current amplitude for stimulationwas not changed. Parallel stimulation at neighboring electrodes waspossible. Subject 1 (S1) described the words as drops falling intowater, or a boat cruising through the water. For some words S1 describedpitch changes and two beats. Seven other test subjects were exposed tothe same stimulation paradigm. Because of the distortion of thefrequency map we simplified the test an asked the test subject toperform a four-word forced-choice test: 21 recorded tracks were playedto the test subject and after each track they were asked to select thecorrect word, as shown in FIG. 9. In about 32% they selected the correctword, as shown in FIG. 9. If two independent presentations of the wordswere combined the number of correct choices (or correct twice) thepercentage was 17%. Interestingly, for the wrong associations, 38% ofthe same words were selected wrong twice. Even more interestingly, somewords were selected by almost all test subjects, as shown in FIG. 9. Ina detailed analysis of the experiments we realized that importantacoustic information below 1 kHz and above 2 kHz was not presented tothe CI user. To determine how such a distorted acoustic signal soundswhen played to a normal hearing subject, we limited the acoustic contentof the words to the frequency range, which was available to the CI usersduring testing. Low frequency information was largely missing. The wordswere unintelligible, but obviously contained lexical content, and theacoustic contend matched very well the hearing experience described bythe two CI users. Encouraged by our initial results, we will optimizethe experimental parameters for a larger series of CI users to betested. The improvement includes the recording of spike sequencesobtained from the inferior colliculus of a more suitable test animal.Chinchilla will be used, which have a hearing range from 0.02 to 16 kHz,similar to that of humans. While words and sentences of the SIN-test areplayed to the ear neural activity will be recorded from the ICC. Neuralactivity from more units in the correct frequency range can be selected.Selection will be done according to the spectrograms of the speechsignals. By better matching the best frequency of the neurons contentwith the spectrograms one expects that the CI user is able to understandsingle words.

In brief, the invention, among other things, discloses the novel codingstrategy, where excitation patterns to be used were recorded from theauditory nerve of a normal functioning cochlea and are stored in aneural code library. Matching an acoustic fingerprint directly with anexcitation pattern along the cochlear implant electrode increases thenumber of independent channels for information transfer, and lead tostochastic firing of the nerve fibers and a significant decrease inpower required by the device. Since the increase in pulse rate is notevoked by the increase in current level but by the timing the pulses arepresented by the generator the current level can always be held atthreshold level, and decrease the power consumption of the cochlearimplant.

The foregoing description of the exemplary embodiments of the inventionhas been presented only for the purposes of illustration and descriptionand is not intended to be exhaustive or to limit the invention to theprecise forms disclosed. Many modifications and variations are possiblein light of the above teaching.

For example, while the description above is primarily directed tosystems and methods related to coding strategies for use with cochlearimplants, it is understood that the solutions provided herein arewell-suited for use across a wide range of neuromodulation devices.

As one example, a method for neuromodulation device coding may be usedin connection with a variety of stimulations of a patient. In such anexample, the method may be implemented according to the following steps:

identifying a pattern of a sensory signal;

retrieving one or more digital representations of a neural responsecorresponding to the identified pattern from a neural code library,wherein the neural code library comprises a plurality of digitalrepresentations of neural responses of neural units corresponding toeach pattern;

selecting one or more neural units of a patient to be stimulated by aneuromodulation device associated with the patient based on theretrieved one or more digital representations of a neural responsepattern; and

stimulating the selected neural units of the patient.

In an application of haptic stimulation, the neuromodulation devicecoding method can be used to stimulate the perception and manipulationof human subjects using a neural code library that comprises a pluralityof digital representations of neural responses of neural units ofanimals or humans in response to the receipt of touching signals.

As another example, the following method for neuromodulation devicecoding for stimulation of an epilepsy patient may be used in connectionwith a closed loop deep brain stimulator. In such an example, the methodmay be implemented according to the following steps:

identifying a pattern of pathological neural activity that heralds thebeginning of epileptic activity;

retrieving one or more digital representations of a neural responsecorresponding to a therapeutic neural response for the identifiedpathological neural activity from a neural code library, wherein theneural code library comprises a digital representation of appropriatetherapeutic neural responses that correspond to a library recorded froman animal, or a library algorithmically reconstructed to mimic thelibrary based on the library recorded from an animal;

selecting one or more neural units of a patient to be stimulated by thedeep brain stimulator associated with the patient based on the retrievedone or more digital representations of a neural response pattern; and

using the deep brain stimulator to stimulate the selected neural unitsof the patient.

As yet another example, the following method for neuromodulation devicecoding for stimulation of a patient may be used in connection with adeep brain stimulator. In such an example, the method may be implementedaccording to the following steps:

either the patient or the neuromodulation device selects a desiredneural signal from a neural code library;

retrieving one or more digital representations of a neural responsecorresponding to the identified pattern from a neural code library,wherein the neural code library comprises a digital representations of aneural response of neural units corresponding to each pattern;

selecting one or more neural units of a patient to be stimulated by thedeep brain stimulator associated with the patient based on the retrievedone or more digital representations of a neural response pattern; and

using the deep brain stimulator to stimulate the selected neural unitsof the patient.

As a further example, the following method for neuromodulation devicecoding for stimulation of a patient may be used in connection with aspinal cord stimulator. In such an example, the method may beimplemented according to the following steps:

either the patient or the neuromodulation device selects a desiredneural signal from a neural code library;

retrieving one or more digital representations of a neural responsecorresponding to the identified pattern from a neural code library,wherein the neural code library comprises a digital representations of aneural response of neural units corresponding to each pattern;

selecting one or more neural units of a patient to be stimulated by thedeep brain stimulator associated with the patient based on the retrievedone or more digital representations of a neural response pattern; and

using the spinal cord stimulator to stimulate the selected neural unitsof the patient.

As yet a further example, the following method for neuromodulationdevice coding for stimulation of a patient may be used in connectionwith a retinal implant. In such an example, the method may beimplemented according to the following steps:

a visual signal is analyzed by a computer vision algorithm which is usedto identify and select items from a scene based upon the patient'spreselected scene complexity parameters;

the algorithm retrieves one or more digital representations of the sceneitems corresponding to the identified pattern from a neural codelibrary, wherein the neural code library comprises digitalrepresentations of neural response of neural units corresponding to eachpattern;

selecting one or more neural units of a patient to be stimulated by theretinal implant associated with the patient based on the retrieved oneor more digital representations of a neural response pattern; and

using the retinal implant to stimulate the selected neural units of thepatient.

The embodiments were chosen and described in order to explain theprinciples of the invention and their practical application so as toenable others skilled in the art to utilize the invention and variousembodiments and with various modifications as are suited to theparticular use contemplated. Alternative embodiments will becomeapparent to those skilled in the art to which the invention pertainswithout departing from its spirit and scope. Accordingly, the scope ofthe invention is defined by the appended claims rather than theforegoing description and the exemplary embodiments described therein.

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What is claimed is:
 1. A method for neuromodulation device coding for stimulation of a patient, the method comprising: (a) identifying a pattern of a sensory signal; (b) retrieving one or more digital representations of a neural response corresponding to the identified pattern from a neural code library, wherein the neural code library comprises a plurality of digital representations of neural responses of neural units corresponding to each pattern; (c) selecting one or more neural units of a patient to be stimulated by a neuromodulation device associated with the patient based on the retrieved one or more digital representations of a neural response pattern; and (d) stimulating the selected neural units of the patient.
 2. The method of claim 1, wherein the nueromodulation device is implanted in the patient.
 3. The method of claim 2, wherein the neuromodulation device is a cochlear implant.
 4. The method of claim 1, wherein the plurality of digital representations of neural responses of neural units are derived from the measured neural responses of neural units in a normal functioning animal.
 5. The method of claim 4, wherein each neural unit is an identifiable and stable single unit in at least one of the inferior colliculus, cochlear nucleus, auditory nerve, haptics, brainstem, and midbrain of the normal functioning animal.
 6. The method of claim 4, wherein the neural responses of neural units of the normal functioning animal are acquired from neural recordings in the neural units when each pattern is played to the normal functioning animal at a plurality of frequencies.
 7. The method of claim 6, wherein each neural response pattern contains a sequence of neural responses of a corresponding neural unit at its corresponding best frequency to the identified pattern.
 8. The method of claim 7, wherein the neural response of each neural unit of the normal functioning animal comprises a train of action potentials generated in the neural unit of the normal functioning animal in response to the receipt of the identified pattern.
 9. The method of claim 8, wherein the selected neural units of the patient are simulated with electrical pulses that are coincident with the trains of action potentials generated in the corresponding neural units of the normal functioning animal in response to the receipt of the identified pattern.
 10. The method of claim 1, wherein the pattern is a segment or a frame of the sensory signal.
 11. The method of claim 10, wherein the sensory signal is an acoustic signal.
 12. The method of claim 11, wherein the pattern comprises a phoneme, a syllable, a word, words, or a combination thereof.
 13. The method of claim 1, further comprising repeating steps (a)-(d) consecutively for each pattern of the sensory signal.
 14. The method of claim 11, wherein the step of identifying the pattern of the sensory signal is performed with a speech recognition algorithm.
 15. The method of claim 1, wherein the step of retrieving one or more digital representations of a neural response corresponding to the identified pattern from the neural code library is performed with a stochastic model including a Hidden Markov Model (HMM).
 16. The method of claim 1, further comprising optimizing the selection of the units for stimulation optimization based on feedback from the patient.
 17. A system for cochlear implant coding for stimulation of a patient, comprising: (a) a neural code library comprising a plurality of digital representations of neural responses of neural units of at least one normal hearing animal, wherein each of the plurality of digital representations of neural responses corresponds to one of a plurality of digital representations of acoustic patterns; (b) a sensory device configured to detect an acoustic signal; (c) an analyzer in communication with the sensory device, configured to identify one of the digital representations of acoustic patterns in the neural code library in response to the receipt of the acoustic signal; (d) a processing device in communication with the neural code library and the analyzer, configured, for each identified acoustic pattern, to retrieve the digital representations of neural responses of neural units corresponding to the identified acoustic pattern from the neural code library; and select one or more neural units of a patient to be stimulated by a cochlear implant implanted in the patient; and (e) a transmitting device in communication with the processing device and the cochlear implant, configured to stimulate the selected one or more neural units of the patient.
 18. The system of claim 17, wherein the digital representations of the neural responses include data for each of a plurality of frequencies.
 19. The system of claim 18, wherein each neural response pattern contains a sequence of neural responses of a corresponding neural unit at its corresponding best frequency to the identified acoustic pattern.
 20. The system of claim 19, wherein the best frequency of a neural unit is a frequency for pure tone stimulation that needs a lowest sound level to evoke a neural response in the corresponding unit.
 21. The system of claim 17, wherein each neural unit is an identifiable and stable single unit in at least one of the inferior colliculus, cochlear nucleus, auditory nerve, brainstem, and midbrain of the normal hearing animal.
 22. The system of claim 17, wherein the neural responses of neural units of the normal hearing animal are acquired from neural recordings in the neural units when each acoustical pattern is played to the normal hearing animal at a plurality of frequencies.
 23. The system of claim 22, wherein the neural response of each neural unit of the normal hearing animal comprises a train of action potentials generated in the neural unit of the normal hearing animal in response to the receipt of the acoustical pattern.
 24. The system of claim 23, wherein the selected one or more neural units of the patient are stimulated with electrical pulses that are coincident with the trains of action potentials generated in the corresponding neural units of the normal hearing animal in response to the receipt of the acoustical pattern.
 25. The system of claim 17, wherein the acoustical pattern is a segment or a frame of the acoustical signal.
 26. The system of claim 25, wherein the acoustical pattern comprises a phoneme, a syllable, a word, words, or a combination thereof.
 27. The system of claim 17, wherein the sensory device comprises one or more microphones.
 28. The system of claim 17, wherein the electrodes of the cochlear implant comprise an electrode array with multiple electrode contacts.
 29. The system of claim 17, wherein the processing device is in communication with the neural code library and the analyzer via a wired or cable connection, or a wireless connection.
 30. The system of claim 17, wherein the transmitting device is in communication with the processing device and the cochlear implant via a wired or cable connection, or a wireless connection.
 31. The system of claim 30, wherein the transmitting device is a wireless transmitting device.
 32. The system of claim 31, wherein the wireless transmitting device is a Bluetooth wireless transmitter.
 33. A method for playing an acoustical signal to a patient having a cochlear implant implanted, comprising: (a) selecting an acoustical signal which the patient wants to listen to; (b) looking up a neural code library, wherein the neural code library comprises a plurality of digital representations of neural responses of neural units of at least one normal hearing animal, wherein each of the plurality of digital representations of neural responses corresponds to one of a plurality of digital representations of acoustic signals and comprises a neural response pattern; (c) if the selected acoustical signal exists in the neural code library, selecting one or more neural units of a patient to be stimulated by a cochlear implant implanted in the patient; and (d) stimulating the selected one or more neural units of the patient for listening to the acoustical signal.
 34. The method of claim 33, wherein each neural unit is an identifiable and stable single unit in at least one of the inferior colliculus, cochlear nucleus, auditory nerve, brainstem, and midbrain of the normal hearing animal.
 35. The method of claim 33, wherein the neural responses of neural units of the normal hearing animal are acquired from neural recordings in the neural units when each acoustical signal is played to the normal hearing animal at a plurality of frequencies.
 36. The method of claim 35, wherein each neural response pattern comprises a train of action potentials generated in a corresponding neural unit of the normal hearing animal in response to the receipt of the acoustical signal.
 37. The method of claim 30, wherein the selected one or more neural units of the patient are stimulated with electrical pulses that are coincident with the trains of action potentials generated in the corresponding neural units of the normal hearing animal in response to the receipt of the acoustical signal.
 38. The method of claim 27, further comprising optimizing the selection of the units for stimulation optimization based on feedback from the patient.
 39. A system for playing an acoustic signal to a patient having a cochlear implant implanted comprising: (a) a neural code library comprising a plurality of digital representations of neural responses of neural units of at least one normal hearing animal, wherein each of the plurality of digital representations of neural responses corresponds to one of a plurality of digital representations of acoustic patterns and comprises a neural response pattern; (d) a processing device in communication with the neural code library, configured to look up the neural code library, and if the selected acoustical signal exists in the neural code library, select one or more neural units of a patient to be stimulated by a cochlear implant implanted in the patient; and (e) a transmitting device in communication with the processing device and the cochlear implant, configured to stimulate the selected one or more neural units of the patient for listening to the acoustical signal.
 40. The system of claim 39, wherein each neural unit is an identifiable and stable single unit in at least one of the inferior colliculus, cochlear nucleus, auditory nerve, brainstem, and midbrain of the normal hearing animal.
 41. The system of claim 40, wherein the neural responses of neural units of the normal hearing animal are acquired from neural recordings in the neural units when each acoustical pattern is played to the normal hearing animal at a plurality of frequencies.
 42. The system of claim 41, wherein each neural response pattern comprises a train of action potentials generated in a corresponding neural unit of the normal hearing animal in response to the receipt of the acoustical signal.
 43. The system of claim 42, wherein the selected one or more neural units of the patient are stimulated with electrical pulses that are coincident with the train of action potentials generated in the neural unit of the normal hearing animal in response to the receipt of the acoustical signal.
 44. The system of claim 39, wherein the electrodes of the cochlear implant comprise an electrode array with multiple electrode contacts.
 45. The system of claim 39, wherein the processing device is in communication with the neural code library via a wired or cable connection, or a wireless connection.
 46. The system of claim 39, wherein the transmitting device is in communication with the processing device and the cochlear implant via a wired or cable connection, or a wireless connection.
 47. The system of claim 46, wherein the transmitting device is a wireless transmitting device.
 48. The system of claim 47, wherein the wireless transmitting device is a Bluetooth wireless transmitter. 